Data engineering interview questions · medium
Transformation vs. Action in PySpark?
What Hadoop command would you use to merge multiple files into one?
What are Spark Submit properties?
What are the key differences between Map and Reduce in Spark?
What are the key performance tuning techniques you apply in Spark jobs to improve performance?
What are the limitations of the REORG command with respect to large datasets?
What are the performance trade-offs of using salting to mitigate data skewness?
What are the steps to efficiently process 1 TB of data in Spark?
What causes Out of Memory (OOM) issues in Databricks, and how do you resolve them?
What causes data skewness in Spark, and how can it be resolved?
What configuration parameters are critical for enabling AQE effectively?
What determines the maximum parallelism achievable in Databricks?
What do you understand by data shuffling in Spark? Why is it important?
What is Broadcast Join and Why is It Required?
What is Shuffle and How to Handle It in Spark
What is offset management in Kafka?
What is the advantage of caching in PySpark? When and why would you use it?
What is the command to import data from HDFS to Hive?
What is the difference between partitions and repartitions in Spark, and when do you use each?
What is the most common performance bottleneck in Spark jobs, and how would you resolve it?
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The most common Spark interview topics are: the difference between RDDs and DataFrames, transformations vs actions, data skew and how to handle it, partition strategies, shuffle optimization, and the catalyst optimizer. Delta Lake and Structured Streaming are increasingly tested.
If you're targeting mid-to-senior roles at companies processing large datasets, yes. Spark/Big Data questions appear in most data engineering interviews at scale-up and enterprise companies. Even companies using other tools test Spark as a proxy for distributed systems knowledge.
Use Databricks Community Edition (free), Google Colab with PySpark, or local Docker setups. Focus on understanding concepts like partitioning, broadcast joins, and lazy evaluation. Most interview questions test conceptual understanding, not syntax.
Data skew handling and performance tuning are the most challenging areas. Interviewers ask how to diagnose skew in a Spark job, strategies to fix it (salting, repartitioning, broadcast joins), and how to read Spark UI for performance bottlenecks.